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High-Dimensional Probability

By Roman Vershynin

High-Dimensional Probability

By Roman Vershynin

0 - Default Title
Description
'High-Dimensional Probability,' winner of the 2019 PROSE Award in Mathematics, offers an accessible and friendly introduction to key probabilistic methods for mathematical data scientists. Streamlined and updated, this second edition integrates theory, core tools, and modern applications. Concentration inequalities are central, including classical results like Hoeffding's and Chernoff's inequalities, and modern ones like the matrix Bernstein inequality. The book also develops methods based on stochastic processes - Slepian's, Sudakov's, and Dudley's inequalities, generic chaining, and VC-based bounds. Applications include covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, and machine learning. New to this edition are 200 additional exercises, alongside extra hints to assist with self-study. Material on analysis, probability, and linear algebra has been reworked and expanded to help bridge the gap from a typical undergraduate background to a second course in probability.
Product details
Edition:
2
Number of Pages:
346
Release Date:
2026-01-31
Publication Date:
2026-01-15
Publisher:
Cambridge University Press
Languages:
Original: English
ISBN10:
1009490648
ISBN13:
9781009490641
Weight:
846 g
Height:
183 cm
Width:
260 cm
Thickness:
23 cm
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